Electric Vehicle Identification from Behind Smart Meter Data
- URL: http://arxiv.org/abs/2509.19316v1
- Date: Thu, 11 Sep 2025 10:10:26 GMT
- Title: Electric Vehicle Identification from Behind Smart Meter Data
- Authors: Ammar Kamoona, Hui Song, Ali Moradi Amani, Mahdi Jalili, Xinghuo Yu, Peter McTaggart,
- Abstract summary: Electric vehicle (EV) charging loads identification from behind smart meter recordings is an indispensable aspect.<n>This paper addresses the problem of EV charging load identification from low-frequency smart meter using an unsupervised learning approach.<n>It only requires real power consumption data of non-EV users, which are abundant in practice.
- Score: 14.982451748073409
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Electric vehicle (EV) charging loads identification from behind smart meter recordings is an indispensable aspect that enables effective decision-making for energy distributors to reach an informed and intelligent decision about the power grid's reliability. When EV charging happens behind the meter (BTM), the charging occurs on the customer side of the meter, which measures the overall electricity consumption. In other words, the charging of the EV is considered part of the customer's load and not separately measured by the Distribution Network Operators (DNOs). DNOs require complete knowledge about the EV presence in their network. Identifying the EV charging demand is essential to better plan and manage the distribution grid. Unlike supervised methods, this paper addresses the problem of EV charging load identification in a non-nonintrusive manner from low-frequency smart meter using an unsupervised learning approach based on anomaly detection technique. Our approach does not require prior knowledge of EV charging profiles. It only requires real power consumption data of non-EV users, which are abundant in practice. We propose a deep temporal convolution encoding decoding (TAE) network. The TAE is applied to power consumption from smart BTM from Victorian households in Australia, and the TAE shows superior performance in identifying households with EVs.
Related papers
- Profiling Electric Vehicles via Early Charging Voltage Patterns [56.4040698609393]
Electric Vehicles (EVs) are rapidly gaining adoption as a sustainable alternative to fuel-powered vehicles.<n>Recent results showed that attackers may steal energy through tailored relay attacks.<n>One countermeasure is leveraging the EV's fingerprint on the current exchanged during charging.
arXiv Detail & Related papers (2025-06-09T12:57:37Z) - Agent-Based Decentralized Energy Management of EV Charging Station with Solar Photovoltaics via Multi-Agent Reinforcement Learning [4.9855485718502015]
The adoption of Electric Vehicles (EVs) keeps increasing, making energy management of EV charging stations critically important.<n>Previous studies have managed to reduce energy cost of EV charging while maintaining grid stability.<n>We propose a novel Multi-Agent Reinforcement Learning (MARL) approach treating each charger to be an agent and coordinate all the agents in the EV charging station with solar photovoltaics.
arXiv Detail & Related papers (2025-05-24T15:34:37Z) - Uncertainty-Aware Critic Augmentation for Hierarchical Multi-Agent EV Charging Control [9.96602699887327]
We propose HUCA, a novel real-time charging control for regulating energy demands for both the building and EVs.<n>HUCA employs hierarchical actor-critic networks to dynamically reduce electricity costs in buildings, accounting for the needs of EV charging in the dynamic pricing scenario.<n> Experiments on real-world electricity datasets under both simulated certain and uncertain departure scenarios demonstrate that HUCA outperforms baselines in terms of total electricity costs.
arXiv Detail & Related papers (2024-12-23T23:45:45Z) - Online Electric Vehicle Charging Detection Based on Memory-based Transformer using Smart Meter Data [19.865702673783154]
The popularity of Electric Vehicles (EVs) poses unique challenges for grid operators and infrastructure.
One critical aspect is the ability to accurately identify the presence of EV charging in the grid.
We propose a novel unsupervised memory-based transformer (M-TR) that can run in real-time (online) to detect EVs charging from a streaming smart meter.
arXiv Detail & Related papers (2024-08-06T03:19:14Z) - A Deep Q-Learning based Smart Scheduling of EVs for Demand Response in
Smart Grids [0.0]
We propose a model-free solution, leveraging Deep Q-Learning to schedule the charging and discharging activities of EVs within a microgrid.
We adapted the Bellman Equation to assess the value of a state based on specific rewards for EV scheduling actions and used a neural network to estimate Q-values for available actions and the epsilon-greedy algorithm to balance exploitation and exploration to meet the target energy profile.
arXiv Detail & Related papers (2024-01-05T06:04:46Z) - Charge Manipulation Attacks Against Smart Electric Vehicle Charging Stations and Deep Learning-based Detection Mechanisms [49.37592437398933]
"Smart" electric vehicle charging stations (EVCSs) will be a key step toward achieving green transportation.
We investigate charge manipulation attacks (CMAs) against EV charging, in which an attacker manipulates the information exchanged during smart charging operations.
We propose an unsupervised deep learning-based mechanism to detect CMAs by monitoring the parameters involved in EV charging.
arXiv Detail & Related papers (2023-10-18T18:38:59Z) - Federated Reinforcement Learning for Electric Vehicles Charging Control
on Distribution Networks [42.04263644600909]
Multi-agent deep reinforcement learning (MADRL) has proven its effectiveness in EV charging control.
Existing MADRL-based approaches fail to consider the natural power flow of EV charging/discharging in the distribution network.
This paper proposes a novel approach that combines multi-EV charging/discharging with a radial distribution network (RDN) operating under optimal power flow.
arXiv Detail & Related papers (2023-08-17T05:34:46Z) - An Energy Consumption Model for Electrical Vehicle Networks via Extended
Federated-learning [50.85048976506701]
This paper proposes a novel solution to range anxiety based on a federated-learning model.
It is capable of estimating battery consumption and providing energy-efficient route planning for vehicle networks.
arXiv Detail & Related papers (2021-11-13T15:03:44Z) - The impact of online machine-learning methods on long-term investment
decisions and generator utilization in electricity markets [69.68068088508505]
We investigate the impact of eleven offline and five online learning algorithms to predict the electricity demand profile over the next 24h.
We show we can reduce the mean absolute error by 30% using an online algorithm when compared to the best offline algorithm.
We also show that large errors in prediction accuracy have a disproportionate error on investments made over a 17-year time frame.
arXiv Detail & Related papers (2021-03-07T11:28:54Z) - Risk-Aware Energy Scheduling for Edge Computing with Microgrid: A
Multi-Agent Deep Reinforcement Learning Approach [82.6692222294594]
We study a risk-aware energy scheduling problem for a microgrid-powered MEC network.
We derive the solution by applying a multi-agent deep reinforcement learning (MADRL)-based advantage actor-critic (A3C) algorithm with shared neural networks.
arXiv Detail & Related papers (2020-02-21T02:14:38Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.